class_docstr: Shuffler is used to shuffle the data.
  
  data is stored in a flatten array with the shape is (num_node * node_size * step) and the
  shuffle is applied to the `node_size` dimension.
code: def get_reverse_indexes(self, step, indexes):
      mapping = self.get_shuffle_index(step, reverse=False)
      return [mapping[i] for i in indexes]
  
imports: from typing import List, Tuple, Dict
  import numpy
  import torch
label_desc: Use image node_id 5 for calling the Shuffler obj's underlying member method code with example usage: obj.get_reverse_indexes(step, indexes) and returns: unknown